1. Field of the Invention
The present invention relates to compensation for nonlinear distortion and, more particularly, to a predistorter for compensating for nonlinear distortion, applicable to a power amplifier and devices having various nonlinear characteristics, and a method for compensating for nonlinear distortion in a predistorter.
2. Description of the Related Art
Devices such as a power amplifier, an analog-to-digital converter (ADC), a digital-to-analog converter (DAC), or the like, of a base station or a mobile terminal in a mobile communications system have nonlinear distortion characteristics. For example, it is desirous for a power amplifier of a base station or a mobile terminal to only amplify the amplitude of an input signal while maintaining the form of the input signal as it is, but if an input signal has a particular level or higher, the power amplifier operates in a saturation region, having nonlinear distortion characteristics, such that the input signal distorted, rather than being linearly amplified. The nonlinear distortion characteristics are major causes of degraded signal quality and data reliability.
Thus, linearization technologies have been developed in various forms to compensate for nonlinear distortion characteristics of an amplifier to ensure stable and high quality signal transmissions.
Typical linearization methods include a backoff method of operating an amplifier only in a linear region and a predistortion method of predistorting an input signal of a power amplifier in consideration of nonlinear characteristics of the power amplifier to linearize the input signal.
An amplifier, a typical nonlinear device, has characteristics in which efficiency and distortion are conflicting (or reciprocal). For example, a Class A type amplifier has a small nonlinear range, such that it has low distortion but poor efficiency. Namely, in order for the Class A type amplifier to perform amplification to have the same level as that of an amplifier having good efficiency, the amplifier must consume more power, so it may not be desirous to apply the Class A type amplifier to a mobile terminal using a battery as a power source.
A Class C type amplifier has efficiency superior to that of Class A type amplifier, but involves more nonlinear distortion. Thus, this amplifier should employ the backoff method to prevent nonlinear distortion, having shortcomings in that the amplifier should be designed to have an output capacity greater than an intended output. In order to solve this problem, preferably, a device having a predistortion function may be provided at a front stage of the amplifier.
Predistortion methods can be divided into two types: One is performing predistortion by using digital signal processing in a baseband, and the other is connecting an RF amplifier having nonlinear characteristics opposite to RF characteristics of a power amplifier in an RF band to a front stage of the power amplifier to predistort an input signal.
The baseband digital predistortion method is advantageous in that a signal can be easily processed. Among the baseband digital predistortion methods is a method of previously measuring nonlinear characteristics of an amplifier, configuring a look-up table corresponding to an inverse function of the measured nonlinear function, and performing digital predistortion with reference to the configured table. However, the method of performing predistortion by using the look-up table has a problem in which all data in the look-up table should be updated when the characteristics of the amplifier are changed.
Another method, which complements the shortcomings of the predistortion method using a look-up table, is modeling nonlinearity of an amplifier and updating a corresponding parameter when the characteristics of the amplifier are changed. Here, the methods of modeling nonlinearity of the amplifier include various methods, such as a method of using a polynomial, a method of using a volterra series, including even a memory effect, and the like.
In general, in the case of devices having nonlinearity, such as an amplifier or the like, the nonlinear characteristics thereof are changed over time and at varying temperatures, and unless predistortion is performed each time the nonlinear characteristics are changed, performance of the device for predistortion is degraded to again cause severe nonlinear distortion in a signal output from the amplifier.
In order to estimate nonlinear characteristics of an amplifier changing over time and according to an environment as mentioned above, a method for performing digital predistortion by using an adaptive algorithm is used.
According to the adaptive algorithm, a difference between an output signal from a predistortion filter implementing polynomial modeling and a desired output signal is regarded as an error, and the coefficient of the predistortion filter is changed to minimize errors. Even when the characteristics of an amplifier are changed over time or according to a specific environment, the predistorter continues to update the coefficient to perform predistortion, thereby compensating for the nonlinear distortion of the amplifier.
As mentioned above, in updating filter parameters of the predistorter by the adaptive algorithm, the parameters must be updated such that the error, i.e., the difference between the output signal from the predistortion device and the desired output signal, can be minimized. However, in actuality, there is no method for accurately recognizing the desired output signal with respect to the output device of the predistorter, it may be difficult to update the filter coefficients of the predistorter with the general adaptive algorithm.
Thus, in order to complement such shortcomings, an indirect learning structure and algorithm have been proposed. According to the indirect learning method, in order to obtain a desired predistorter output signal, a predistortion filter, which is the same as that of the predistorter, is installed in a portion to which an output from an amplifier is fed back, and an output from the predistorter using the same filter as that of the filter is regarded as a desired output signal and adaptive, whereby the predistorter has an inverse function role in linearizing nonlinearity of the amplifier.
However, the indirect learning method is disadvantageous, in that the order of the filter is not accurately used in the process of calculating and adapting an error by using the indirect method, without actually using a desired predistorter output signal, and when the distortion of the amplifier is severe, performance may be degraded.
In order to complement the shortcomings, a method of implementing a predistorter by expressing an input signal of a predistorter, the coefficient of an estimated amplifier, and a present and past signal of the predistorter by modeling nonlinear characteristics of an amplifier, estimating the coefficients, and changing an input/output relational expression of the amplifier, rather than indirectly adapting the predistorter, has been introduced. This method, however, has shortcomings in that linearization performance is not guaranteed in a range in which nonlinearity is severe. Namely, there is a problem in which a backoff level must be more severely adapted in comparison to the indirect learning method.